Methods of Investigating Formation Samples Using NMR Data

ABSTRACT

A methods are provided for investigating a sample containing hydrocarbons by subjecting the sample to a nuclear magnetic resonance (NMR) sequence using NMR equipment, using the NMR equipment to detect signals from the sample in response to the NMR sequence, analyzing the signals to extract a distribution of relaxation times (or diffusions), and computing a value for a parameter of the sample as a function of at least one of the relaxation times (or diffusions), wherein the computing utilizes a correction factor that modifies the value for the parameter as a function of relaxation time for at least short relaxation times (or as a function of diffusion for at least large diffusion coefficients).

CROSS-REFERENCE TO RELATED APPLICATION

This application is a non-provisional patent application that claims benefit of U.S. Provisional Patent Application Ser. No. 61/657,527 filed Jun. 8, 2012 entitled “Method to Estimate Porosity from NMR Data at Short Relaxation Times.” The provisional patent application is incorporated by reference herein.

BACKGROUND 1. Field

This application relates to methods for processing data obtained from nuclear magnetic resonance (NMR) equipment that is used to investigate earth samples containing hydrocarbons. This application more particularly relates to methods for processing NMR tool data obtained from investigating formations containing hydrocarbons that exhibit short relaxation times in order to obtain information regarding one or more characteristics of the formation, although it is not limited thereto.

2. State of the Art

Nuclear magnetic resonance (NMR) tool are used in oilfield applications to enable characterization of petrophysical properties. Low-field relaxation measurements made by the NMR tools are often dominated by short relaxation components, typically on the order of the echo-spacing of the NMR pulse sequence. For example, bulk relaxation of heavy oils often falls below 100 msec. Recent studies indicate that Barnett gas shales contain organic matter in the form of kerogen in various stages of maturation, see C. H. Songergold, et al., “Microstructural Studies of Gas-Shales”, SPWLA 50^(th) Annual Logging Symposium, 2009, and that there is significant porosity in small pore sizes of the organic matter varying between 5-1000 nm. This results in significant T₂ relaxation below 10 msec. Laboratory studies on Haynesville gas-shale cores also provide experimental evidence that fluid is restricted in nanopores resulting in T₂ relaxations that are smaller than a few milliseconds. R. Kaushik et al., “Characterization of Gas Dynamics in Kerogen Nanopores by NMR”, SPE-147198, 2011.

Traditionally, pulse sequences and data inversion are optimized for magnetization data that fall in the middle of the T₂ relaxation spectrum; i.e., between 50 ms and 500 ms. To enhance the precision at short relaxation times, data has been acquired according to an “enhanced precision mode” (EPM—a Trademark of Schlumberger). See, D. McKeon et al. “An improved NMR tool design for fasting logging”, SPWLA, 1999. This mode involves acquiring magnetization data at at least two different wait times often referred to as “main” and “burst”. In addition, improved precision in porosity determinations has been obtained by increasing the number of repeats, and thus improving the signal-to-noise ratio (SNR). See, P. Hook et al., “Improved Precision Magnetic Resonance Acquisition: Application to Shale Evaluation”, SPE-146883, 2011.

SUMMARY

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

According to one aspect, methods are provided for investigating a sample containing hydrocarbons by subjecting the sample to a nuclear magnetic resonance (NMR) sequence using NMR equipment, using the NMR equipment to detect signals from the sample in response to the NMR sequence, analyzing a decay of the signals to extract a distribution of relaxation times, and computing a value for a parameter of the sample as a function of at least one of the relaxation times, wherein the computing utilizes a correction factor that modifies the value for the parameter as a function of relaxation time for at least some short relaxation times, on the same order of magnitude as the echo-spacing of the tool. For purposes herein, the “same order of magnitude” includes a range from one-half the echo spacing to ten times the echo spacing.

In one embodiment, an estimate of porosity of a formation containing hydrocarbons is made by placing an NMR tool in a borehole traversing the formations, conducting downhole experiments to obtain signals over time, analyzing the decay of the signals to extract a T₂ distribution, and computing a porosity estimate as a function of T₂, where the computing utilizes a correction factor that is a function of relaxation time for at least short relaxation times.

In one embodiment, the correction factor that is used in computing the parameter as a function of relaxation time for at least short relaxation times is a function of normalized bias of the measurement. In one embodiment, the normalized bias of the tool is obtained from the porosity sensitivity curve. For computation of porosity sensitivity curve, see H, N. Bachman et al, “Porosity determination from NMR log data: the effects of acquisition parameters, noise and inversion”, SPE-110803, 2007. In another embodiment, the normalized bias is obtained by testing the tool with respect to samples of known parameter values.

In one aspect, improved porosity estimates obtained through the use of a correction factor obtained from the porosity sensitivity curve can be utilized to obtain improved determinations of additional parameters such as, by way of example only, rock permeability, hydrocarbon viscosity, bound and free fluid volumes, etc.

In another aspect, improved porosity estimates obtained through the use of a correction factor that is a function of relaxation time can be used to obtain improved analysis of organic content of a formation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is porosity sensitivity plot as a function of T₂.

FIG. 2A is a graph showing a true T₂ distribution.

FIG. 2B shows data simulated through an enhanced precision mode based on the T₂ distribution of FIG. 2A.

FIG. 2C is a graph showing an estimated T₂ distribution using the data of FIG. 2A generated using an inverse Laplace Transform technique (ILT) and the methods of this application (NSA).

FIG. 2D is a chart showing true porosity and T_(2LM) values and values obtained via ILT and NSA.

FIG. 3 is a plot of five models of T₂ distribution with short relaxation times from which magnetization data are simulated.

FIG. 4 shows sensitivity curves for 10 and 50 repetitions of a burst.

FIG. 5 is a plot of five models of T₂ distributions with intermediate and long relaxation times from which magnetization data are simulated.

FIG. 6 is a workflow for using an improved determination of porosity to obtain improved determinations of further parameters.

FIG. 7 is a series of graphs showing a true T₂ distribution and distributions calculated using different processing techniques with and without the improved determination of porosity as inputs thereto.

FIG. 8 is a composite view of a shale section which shows separation between density porosity (DPHI) and NMR porosity (TCMR) where the kerogen is present.

FIG. 9 is a composite view of another well with an appreciable shale section (in the upper, middle and lower levels) which shows separation between DPHI and TCMR where the kerogen is present.

FIGS. 10A and 10B are a total organic carbon (TOC) estimation in organic shale wherein kerogen density RHOk, =1.35 g/cc, maturity constant Kvr=1.2, hydrogen index of the pore fluid HIf=1, and apparent fluid density in total pore volume RHOF=0.8 g/cc.

FIGS. 11A, 11B, and 11C are a plot of three ELANs which were run with the same model and then compared to NMR porosity.

FIGS. 12A, 12B, 12C, and 12D are an ELAN model that shows montmorillonite that was based on and elemental capture spectroscopy (ECS) and triple combo log.

DETAILED DESCRIPTION

A porosity sensitivity curve is seen in FIG. 1. The porosity sensitivity curve is a plot of the estimated porosity obtained from applying with an NMR tool a given pulse sequence, and utilizing a set of acquisition and inversion parameters on the received signal. A porosity sensitivity curve is obtained as follows. For each location of the Dirac-delta function in the T₂ domain, the magnetization data with porosity ϕ_(T) are simulated with white Gaussian noise with zero-mean and a standard deviation σ_(ε). The SNR for the data set is ϕ_(T)/σ_(ε). The sensitivity curve can be computed a priori and is a function of the pulse sequence (e.g., fully polarized CPMG (Carr-Purcell-Meiboom-Gill), EPM (enhanced precision mode), etc.), data acquisition parameters (e.g., wait times, number of bursts, number of repeats, cable speed, SNR in the data) and inversion parameters (e.g., regularization, lower and upper limits of the discretized relaxation times, the level of discretization, the T₁/T₂ ratio).

The measured NMR data are then processed according to an algorithm, such as the Inverse Laplace Transform (ILT) to estimate the T₂ distribution ϕ(T₂). A description of the ILT algorithm, is provided in H. N. Bachman et al, “Porosity determination from NMR log data: the effect of acquisition parameters, noise, and inversion” SPE-110803, 2007 which is hereby incorporated by reference herein in its entirety. The ILT algorithm or method involves minimizing a cost function Q with respect to the underlying T₂ distribution ϕ according to

Q=∥G−Lϕ∥ ²+α∥ϕ∥²  (1)

where G is a vector representing the measured EPM data, L is the matrix relating the T₂ distribution to the NMR measurement, and ϕ is the discretized version of the underlying density function ϕ(T₂). The first term in the cost function is the least squares error between the data and the fit. The second term is referred to as the regularization and incorporates smoothness in the expected relaxation amplitudes. The parameter α denotes the compromise between the fit to the data and an a priori expectation of the distribution. In equation (1), α is the weight given to the regularization and can be chosen by a number of different methods. See N. P. Galatsanos et al, “Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation”, IEEE Transactions in Image Processing, vol. 1, No. 3, July 1992. The total corrected porosity is the sum of the porosity over all T2 bins.

The porosity sensitivity curve of FIG. 1 was generated based on an EPM pulse sequence generated from a Dirac delta function with unity porosity, an echo spacing of 200 μs, a main Carr-Purcell-Meiboom-Gill (CPMG) sequence with 1000 echoes and a wait time of 10 s, and a burst with 50 repetitions, 30 echoes, and a wait time of 20 ms. These echoes were contaminated with many noise realizations of white Gaussain noise with standard deviation 0.2. For each of these realizations, the porosity was estimated using regularized nonnegative least squares with Tikhonov regularization. In this plot, an inverse Laplace transform (ILT) with Tikhonov regularization α=10 was used. The T₂ bins used to analyze the data in FIG. 1 are logarithmically spaced between T_(2,min)=300 μs and T_(2,max)=3 s.

As seen in FIG. 1, at short relaxation times (e.g., under 1 ms, and more particularly under 0.5 ms) there is a decreased sensitivity (accuracy) which is due to finite echo spacing typically on the order of 200 μs and, therefore, a decreased measured signal amplitude. The decreased sensitivity is also due to the noise in the measurement. At large relaxation times, the sensitivity is small due to finite polarization. At intermediate relaxation times, certain undulations in sensitivity are observed. These undulations are a function of how the inversion handles the data and depend on the T₁/T₂ ratios, regularization, and signal to noise ratio (SNR) in the data. Decreased sensitivity at short and long relaxation times, poor SNR in the field-data as well as non-linear aspects of the inversion pose challenges in accurate estimation of parameters from the measured data.

According to one embodiment, the decreased sensitivity at short relaxation times is addressed through the use of a correction factor c_(f)(T₂) that is a function of T₂. In particular, if the estimated porosity from an inversion algorithm is {circumflex over (ϕ)}, then the normalized bias (inaccuracy) B in porosity at a particular relaxation time can be expressed as

$\begin{matrix} {B = \frac{{\langle\hat{\varphi}\rangle} - \varphi_{T}}{\varphi_{T}}} & (2) \end{matrix}$

where ϕ_(T) is the true porosity and where <⋅> is an average computed over many different realizations of noise. In one embodiment, the normalized bias B can be computed from the mean of the porosity obtained from multiple realizations of the data as the location of the Dirac-delta function systematically scans the T₂ spectrum. Similarly, the standard deviation σ_(ϕ) (or error bar) of the estimated porosity as shown in FIG. 1 can be computed a priori for each location of the Dirac-delta function. Together, the bias and the standard deviation can be used to compute a correction factor c_(f) to the estimated T₂ distribution and porosity as follows. In another embodiment, the normalized bias B is obtained by testing the tool with respect to one or more samples of known parameter values (i.e., one or more calibration samples.

Consider a measured magnetization decay, obtained and analyzed using the same acquisition and inversion parameters used to derive the porosity sensitivity curve. Let {circumflex over (ϕ)}(T₂) obtained from the non-linear analysis denote the ‘binned porosity”, referring to the estimated T₂ distribution for a specified relaxation time T₂. The normalized error can be related to the previously computed normalized bias according to

$\begin{matrix} {{B\left( T_{2} \right)} \approx {\frac{{\varphi \left( T_{2} \right)} - \varphi_{T}}{\varphi_{T}}.}} & (3) \end{matrix}$

Thus, the corrected porosity ϕ_(c) is expressed by

$\begin{matrix} {{{\varphi_{c}\left( T_{2} \right)} \approx \varphi_{T}} = {\frac{\hat{\varphi}\left( T_{2} \right)}{1 + {B\left( T_{2} \right)}}.}} & (4) \end{matrix}$

From relationship (4), it can be seen that a correction factor c_(f)(T₂) can be computed as

c _(f)(T ₂)=1/(1+B(T ₂)).  (5)

Therefore, a more accurate estimate of porosity at any relaxation time T₂ is

{circumflex over (ϕ)}_(c)(T ₂)=c _(f)(T ₂){circumflex over (ϕ)}(T ₂).  (6)

The corrected porosity is the sum over all the corrected porosity in all T₂ bins.

In one embodiment, the role of the correction factor of equation (6) is to amplify the binned porosity where it tends to be under-estimated. In one embodiment, the role of the correction factor of equation (6) is to amplify the binned porosity where it tends to be under-estimated and to reduce the porosity where it is over-estimated. This results in a more uniform sensitivity and accurate estimation of the binned and total porosity over the range of the T₂ spectrum.

An alternate expression for the correction factor can be obtained by taking into account the SNR of the T₂ distribution at a given T₂ according to

$\begin{matrix} {{{c_{f}\left( T_{2} \right)} = \frac{1}{1 + {{B\left( T_{2} \right)}\frac{R\left( T_{2} \right)}{{\beta {\langle{R\left( T_{2} \right)}\rangle}} + {R\left( T_{2} \right)}}}}}{{{where}\mspace{14mu} {R\left( T_{2} \right)}} = {\frac{\hat{\varphi}\left( T_{2} \right)}{\sigma_{\varphi}\left( T_{2} \right)}.}}} & (7) \end{matrix}$

Here R(T₂) corresponds to the SNR for a given T₂, β is a scalar whose magnitude is typically on the order of unity, and < > is an average computed over T₂. When the SNR at any relaxation time is large (signifying high confidence in the presence of that T₂ component in the data), the alternate correction factor of equation (7) can be significant and modifies the binned porosity. However, when the SNR at any relaxation time is small (signifying low confidence at that relaxation time), the correction factor tends to a value of 1 and does not appreciably modify the binned porosity.

The use of the SNR in the correction factor (i.e., the alternate correction factor of equation (7)) has particular impact in embodiment having T₂ distributions that do not have short relaxation times since the alternate correction factor avoids amplifying artifacts at short relaxation times obtained in the estimated T₂ distribution as a result of the non-linear aspects of the inversion algorithm.

The impact of a correction factor, according to equation (7) is seen with reference to FIGS. 2A-2D. In particular, FIG. 2A shows a T₂ distribution from which magnetization data are simulated in EPM mode. The true porosity of the T₂ distribution (as indicated in FIG. 2D) is 11.0 pu and the simulated main and burst data with additive noise with SNR=5 is shown in FIG. 2B based on an echo spacing of 0.2 ms, a main CPMG with 1800 echoes and a wait time of 2.4 s, and a burst with 10 repetitions, 30 echoes, and a wait time of 20 ms. The results of the analysis on a data set using an inverse Laplace transform (ILT) with standard inversion parameters and α=10 assuming 30 bins for the T₂ distribution logarithmically spaced between T_(2,min)=300 μs and T_(2,max)=3 s is seen in FIG. 2C. Also seen in FIG. 2C are the results of an analysis using the same inversion parameters but correcting the ILT with the correction factor (shown and referred to hereinafter as NSA). It is seen that the components corresponding to short relaxation times are enhanced to a small degree with the T₂ distribution at intermediate relaxation times has undergone at most negligible change. FIG. 2D shows the true porosity and the true T₂ logarithmic mean T_(2,LM,true) as well as the porosity and T_(2,LM) calculated by the ILT analysis ({circumflex over (ϕ)} and {circumflex over (T)}_(2,LM)) and the NSA analysis ({circumflex over (ϕ)}_(c) and {circumflex over (T)}_(2,LM,c)). The error bars on the porosity are obtained by Monte-Carlo analysis on data with different noise realizations. As will be appreciated, the porosity calculated by the NSA analysis using the correction factor is much closer to the true porosity ϕ_(true) than the porosity calculated by the ILT analysis, and the T₂ logarithmic mean is also significantly closer to the true value.

If the normalized root mean square error (NRMSE or e) of the porosity is defined according to

$\begin{matrix} {e = {\frac{\sqrt{\langle\left( {\hat{\varphi} - \varphi_{T}} \right)^{2}\rangle}}{\varphi_{T}} \cdot 100}} & (9) \end{matrix}$

and 100 different noise realizations of the data were obtained from the T₂ distribution in FIG. 2A and analyzed using ILT and NSA, using the calculated values set out in FIG. 2D, the NRMSE for the ILT-derived porosity is 10.4% whereas the NRMSE for the corrected NSA-derived porosity was a lower 7.6%.

Turning to FIG. 3, a plot of five models of T₂ distributions with short relaxation times from which magnetization data are simulated is provided. The data are simulated in EPM mode with a SNR=5. The data are calculated with an echo spacing of 0.2 ms, a main CPMG with 1800 echoes and a wait time of 2.4 s, and a burst with 30 echoes and a wait time of 20 ms. The burst is repeated 10 and 50 times (i.e., N_(r)=10 or 50) to provide different sets of data. Data are analyzed using ILT or NSA, assuming 30 bins for the T₂ distribution logarithmically spaced between T_(2,min)=300 μs and T_(2,max)=3 s and an automated regularization parameter. See L. Venkataramanan et al., “Solving Fredholm Integrals of the First Kind with Tensor Product Structure in 2 and 2.5 Dimensions, IEEE Transactions on Signal Processing, 50:1017-1026 (2002). The results are seen in Table 1 which compares the NMRSE calculated for the ILT and NSA results (100 different data sets with different realizations of noise) for each of the five models. A difference in NRMSE of more than 3% is significant.

TABLE 1 Model Processing NRMSE with N_(r) = 10 NRMSE with N_(r) = 50 1 NSA 7.8 5.2 ILT 9.8 7.7 2 NSA 8.4 5.4 ILT 7.3 5.5 3 NSA 20 17.3 ILT 28 24.7 4 NSA 18 14 ILT 29 23 5 NSA 9 7.3 ILT 12 10.4

As seen in Table 1, the analysis of data using NSA is significantly better than the ILT analysis in several circumstances for all of the models except model 2, and is never significantly worse.

Columns 3 and 4 of Table 1 show the NRMSE of porosity obtained on data sets with 10 and 50 repeats in the burst, respectively. From Columns 3 and 4 it is seen that a larger number of repeats in the burst leads to an increased SNR and thus a smaller NRMSE, albeit with an increased acquisition time. This is also illustrated in FIG. 4 where the accuracy and precision analyzed with ILT on data with 50 burst repetitions is seen to be better than data acquired with 10 repetitions.

FIG. 5 is a plot of five models of T₂ distributions with intermediate and long relaxation times from which magnetization data are simulated with SNR=5. The EPM pulse sequence is used with 1800 and 30 echoes in the main and burst. The wait times are 2.4 seconds and 20 ms respectively. The echo spacing for the main and burst is 0.2 ms. Data sets with bursts having 10 and 50 repeats were generated.

The data were analyzed using ILT and NSA processing. The default analysis parameters were used with 30 bins in the T₂ domain, T_(2,min)=0.3 ms, T_(2,max)=3 s and automated regularization. The first echo is used in the analysis. The NRMSE obtained from analysis of 100 different data sets simulated from each of the five models is summarized in Table 2. It is seen that for the models having T₂ distributions with intermediate and long relaxation times, the results of ILT and NSA processing are comparable as there is no statistically significant difference between the two.

TABLE 2 Model Processing NRMSE with N_(r) = 10 NRMSE with N_(r) = 50 A NSA 6.9 5.1 ILT 6.6 5.0 B NSA 8.4 6.5 ILT 9.3 8.4 C NSA 6 3.8 ILT 5.6 3.7 D NSA 6.4 4 ILT 5.9 3.9 E NSA 5.3 6.7 ILT 5.2 6.8

According to one embodiment, the results of NSA processing may be used as part of a method of determining a distribution of a variable that is used to find values of at least one parameter of a sample. More particularly, as seen in FIG. 6 and as disclosed in copending U.S. Ser. No. 13/346,468 filed Jan. 9, 2012 (which is hereby incorporated by reference in its entirety), data G(t) 10 indicative of what might have been measured by an NMR tool as a result of having applied a pulse sequence to a sample is processed using the integral transform approach described in U.S. Ser. No. 13/333,232 (filed Dec. 21, 2011, which is hereby incorporated by reference herein in its entirety) to provide a plurality of linear functionals, e.g., (T), Tapered areas 30. Porosity calculations 40 obtained by processing the NMR data G(t) using NSA processing as described above, are used as an input to the integral transform approach processing 20. The linear functionals obtained at 30 are then used as constraints or “priors” in a cost function 50 incorporating them as Laplace transform elements and utilizing indications of the NMR data in order to generate a T₂ distribution denoted f_(NEW-c)(T₂) 60. The calculated T₂ distribution is an answer product that may be used for any of many purposes. By way of example only, the T₂ distribution 60 may be used at 70 to generate an estimate of one or more parameters or properties of the sample. Where the sample is a rock or a formation, the parameters may include parameters such as rock permeability and/or hydrocarbon viscosity, bound and free fluid volumes, among others. The parameters may then be used at 80, if desired, in models, equations, or otherwise to act on the sample, such as in recovering hydrocarbons from the formation.

FIG. 7 compares the true T₂ distribution against the estimated distributions determined using ILT processing, NSA processing, and the processing disclosed in previously incorporated copending U.S. Ser. No. 13/346,468 but utilizing the modified or corrected porosity calculations obtained via NSA processing (NEW-c). As suggested in FIG. 8, the calculated T₂ distribution labeled NEW-c has considerably less bias than the T₂ distribution obtained using ILT processing (i.e., the peak at short T₂'s was closer to the true T₂ distribution). In some cases, better resolution was obtained at higher T₂'s than as determined by ILT processing and by NSA processing, as indicated by the circled area.

According to another embodiment, instead of minimizing a cost function with respect to T₂ to obtain a T₂ distribution estimate, a cost function is minimized with respect to a longitudinal relaxation time (T₁) to obtain a T₁ distribution estimation f(T₁). More specifically, data G(t) is processed using an integral transform approach described in previously incorporated U.S. Ser. No. 13/333,232 to provide a plurality of linear functionals, e.g., <T₁ ^(ω)>, Tapered areas. Porosity calculations obtained by processing the NMR data G(t) according to NSA processing is used as inputs to the integral transform approach processing. The linear functionals obtained are then used as constraints or “priors” in a cost function incorporating them as well as Laplace transform elements and utilizing indications of the NMR data in order to generate a T₁ distribution f_(NEW-c)(T₁). The calculated T₁ distribution can be used for any of many purposes such as to generate an estimate of one or more parameters of the sample.

In a similar manner, according to another embodiment, instead of minimizing the cost function with respect to a relaxation time to obtain a relaxation time distribution estimation, a cost function is minimized with respect to NMR diffusion (D) to obtain a D distribution estimation f(D). More specifically, data G(t) is processed using an integral transform approach described in previously incorporated U.S. Ser. No. 13/333,232 to provide a plurality of linear functionals, e.g., (D^(ω)), Tapered areas. Porosity calculations obtained by processing the NMR data G(t) according to NSA processing is used as inputs to the integral transform approach processing. The linear functionals obtained are then used as constraints or “priors” in a cost function incorporating them as well as Laplace transform elements and utilizing indications of the NMR data in order to generate a D distribution f_(NEW-c)(D). The calculated D distribution can be used for any of many purposes such as to generate an estimate of one or more parameters of the sample.

It will be appreciated that multidimensional distributions may also be extracted using as inputs to the integral transform approach the porosity calculations obtained by processing the NMR data G(t) according to NSA processing.

According to one aspect, NSA processing to obtain improved porosity determinations in samples with short relaxation times may be used to improve an analysis of the organic content of a sample. For example, NSA processing may be used in helping to discriminate organic versus inorganic zones in a formation. More particularly, organic shales, or intervals with total organic carbon exceeding a few percent, are often found intermingled with reservoir and/or non-reservoir intervals which contain no organic matter. Organic zones are identified using NSA total porosity and a matrix adjusted density porosity log available using an Elemental Capture Spectroscopy (ECS) sonde (ECS being a trademark of Schlumberger). See, M. Herron et al., “Real-Time Petrophysical Analysis in Siliciclastics from the Integration of Spectroscopy and Triple-Combo Logging”, SPE Annual Technical Conf. and Exhibition, 29 Sep.-2 Oct. 2002. Although kerogen responds as part of the pore space to density porosity tools, magnetic resonance porosity is insensitive to its presence. Thus, the intervals where the NSA total porosity (as measured by a magnetic resonance tool such as the CMR tool of Schlumberger adapted to conduct NSA processing) is less than matrix adjusted density porosity, an organic shale interval can be assumed to be present.

As another example, NSA processing may be used in conjunction with estimating total organic carbon (TOC) in organic shale reservoirs utilizing the “porosity deficit method”. More particularly, a density log response to matrix volumes can be written as

ρ_(l)=ρ_(m)(1−V _(f) −V _(k))+ρ_(fa) V _(f)+ρ_(k) V _(k)  (10)

where ρ_(l) is the density from the density log in g/cc, ρ_(m) is the dry matrix density obtained from an ECS channel in g/cc, ρ_(fa) is the apparent fluid density (g/cc) in total pore volume, ρ_(k) is the kerogen density (g/cc), V_(f) is the volume hydrocarbon plus total water (V/V), and V_(k) is the volume kerogen plus unseen bitumen (V/V). The NSA magnetic resonance porosity can be written as

TCMR_(nsa) =V _(f) HI _(f)  (11)

where TCMR is the total porosity from the Combinable Magnetic Resonance (CMR) tool (CMR is a trademark of Schlumberger) adapted to conduct NSA processing, and HI_(f) is the hydrogen index of the pore fluid. Combining equations (10) and (11) results in

$\begin{matrix} {V_{k} = {\frac{\rho_{m} - \rho_{l}}{\rho_{m} - \rho_{k}} - \frac{{TCMR}_{nsa}\left( {\rho_{m} - \rho_{f}} \right)}{{HI}_{f}\left( {\rho_{m} - \rho_{k}} \right)}}} & (12) \end{matrix}$

where ρ_(f) is the fluid density.

The kerogen (plus unseen bitumen) volume of equation (11) may be converted to a total organic carbon (TOC) estimation so that comparisons with other estimations of TOC in weight percentage can be made using

$\begin{matrix} {{TOC} = {\frac{V_{k}}{K_{vr}} \cdot \frac{\rho_{k}}{\rho_{l}}}} & (12) \end{matrix}$

where K_(vr) is a maturity constant.

FIG. 8 is a partial log of shale section in a well which shows separation (porosity deficit) between density porosity (DPHI) and TCMR where the kerogen is present. The indicated region where the matrix adjusted density porosity (DPHI) and TCMR are separated reflects potential organic shale. Another log reflects uranium content U which will show an elevated reading in the presence of kerogen. FIG. 9 is a log of a shale section which shows separation between density porosity (DPHI) and TCMR where the kerogen is present. FIGS. 10A and 10B show a log of a TOC estimation in organic shale wherein ρk,=1.35 g/cc, K_(vr)=1.2, HI_(f)=1, and ρ_(f)=0.8 g/cc.

According to another aspect, NSA processing may be used to more accurately determine the likelihood of bitumen in a sample. More particularly, significant fractions of bitumen (>5% bulk volume) of the rock are sometimes present in immature organic shale reservoirs. The magnetic resonance determination with NSA processing of total porosity is insensitive to bitumen, and thus, the TOC estimation from the porosity deficit method detailed in equations 10 through equation 12 above includes unseen bitumen. If the TOC estimation from the porosity deficit method is much greater than the TOC estimation from empirical methods, bitumen is likely present. See, Schmoker, J. W. and Hester, T. C, Organic Carbon in Bakken Formation, United States Portion of the Williston Basin, AAPG Bulletin, Vol. 67, No. 12, 2165-2174 (1983).

According to another aspect, the NSA processed porosity determination can be used to provide an indication of thermal maturity of the kerogen in the sample. In particular, intervals dominated by organic hosted porosity are identified when TOC estimations from empirical methods match estimations from the porosity deficit method. Intervals dominated by kerogen hosted porosity have very short T₂ times, with the majority of the porosity no more than 10 ms. Conversely, intervals associated with non-kerogen hosted porosity are identified when TOC estimations from density regressions overestimate estimations from the porosity deficit method. Intervals dominated by non-kerogen hosted porosity have longer T₂ times, with more porosity above 10 ms. It has been observed that the logarithmic mean of the T₂ distribution in intervals dominated by kerogen hosted porosity varies laterally across liquid bearing organic shale reservoirs, and, can be related to maturity. Less mature areas are identified by shorter T_(2LM), while more mature areas are identified by longer T_(2LM).

In another aspect, the NSA processed porosity determination can be used as part of a method of finding montmorillonite in gas or condensate shale reservoirs. More particularly, in gas and condensate bearing reservoirs, the NMR hydrocarbon signal tends to be well separated from the clay bound water signal. The amount of T₂ signal below 3 ms can be used as an estimate of the amount of clay bound water in an inorganic shale, gas condensate, or gas reservoir.

As previously set forth, the NSA analysis has the ability to examine very short T₂ times. The T₂ response associated with the four major clay minerals are as follows:

TABLE 3 Clay Type Measured T₂ at TE .5 ms Wet Clay Porosity (pu??) Montmorillinite .3 to 1 ms ~42 Illite 1 to 2 ms ~10 Kaolinite 8 to 16 ms ~10 Chlorite ~5 ms ~10

While the vast majority of the hydrocarbon signal is found above 3 ms, montmorillonite and illite both have clay bound water T₂ responses below 2 ms as seen in Table 3. However, an appreciable difference in clay bound water exists between illite and montmorillonite. Thus, at the higher clay levels of organic shale reservoirs, the NSA processing is a good mechanism for identifying montmorillonite. Montmorillonite is detected from an elevated wet clay porosity. The wet clay porosity is determined by taking the NMR porosity beneath 3 ms and dividing by the volume of clay obtained from elemental spectroscopy. If the wet clay porosity is greater than 10 PU, then smectite is present. This method is effective in shaly sands, sands where illite and montmorillonite are the dominant clay and no bitumen is present.

FIGS. 11A, 11B, and 11C represent three ELANs (i.e., probabilistic analyses using statistical methods designed for quantitative formation evaluation, where simultaneous equations described by one or more interpretation models are solved with log measurements and response parameters being used together in response equations to compute volumetric results for formation components). The ELANs were run with the same model and then compared to the NMR derived porosity. The Schmoker relationship (see Schmoker, J. W. and Hester, T. C, Organic carbon in Bakken Formation, United States portion of the Williston Basin, AAPG Bulletin, Vol. 67, No. 12, 2165-2174 (1983)) was used as an input for kerogen volume. In the left and middle ELAN logs (highest and middle maturity examples), there is a reasonable match between NMR porosity and ELAN porosity in the last column (solid and dotted black trace) indicating that the NMR is seeing the majority of the hydrocarbon. In addition, there is a large shift in the T₂ distribution to shorter T_(2LM) times from the left figure to the middle figure due to both smaller kerogen pore size and lower gas oil ratio (GOR) which are a result of lower thermal maturity. In the right-most ELAN log (the lowest maturity example), CMR porosity underestimates total porosity (in the last column the dotted black trace is large than the solid black trace). It may therefore be inferred that the T₂ times of the kerogen hosted pores are so short, they are not detected by the CMR tool. Thus, the T₂ distribution in the right-most ELAN log represents primarily the clay and capillary bound water in the formation.

FIGS. 12A, 12B, 12C, and 12D are an ELAN model log that shows montmorillonite that was based on an Elemental Capture Spectroscopy (ECS—a trademark of Schlumberger) log and triple combo log. The wet clay porosity (curve on the right) was estimated by taking the NSA porosity below 3 ms and dividing by the weight of clay from the ECS. When the estimated wet clay porosity from the NSA analysis and ECS exceeds 10 pu, montmorillonite is present. It is noted that the NSA porosity is substantially greater than NMR standard porosity in zones with montmorillonite.

According to another aspect, a quality flag or indicator may be provided to identify intervals of a formation where the NSA analysis may be significantly underestimating the formation porosity. More particularly, the NSA analysis provides a good estimate of formation porosity in shales. However, the porosity still may be underestimated due to extremely short T₂ times that even the NSA analysis cannot account for because the relaxation times are significantly shorter than the echo spacings. By taking the difference in the NSA analysis versus the ILT algorithm, intervals of a formation may be identified where the NSA may have large underestimations of formation porosity. Those intervals are where the difference between porosity as determined by the NSA analysis and the ILT analysis is largest. A quality flag or indicator can be used to identify those locations.

There have been described and illustrated herein several embodiments of methods of investigating a formation using NMR measurements. While particular embodiments and aspects have been described, it is not intended that the disclosure be limited thereto, and it is intended that the claims be as broad in scope as the art will allow and that the specification be read likewise. Thus, while particular correction factors for modifying porosity estimates at short T₂ have been described, it will be appreciated that other correction factors may be utilized. Likewise, while particular pulse sequences have been described for obtaining NMR signals from which a porosity determination can be made, it will be appreciated that other such sequences can be utilized. Further, while the corrected porosity determination (NSA) was described as being useful to obtain improved determinations of particular sample parameters such as rock permeability, hydrocarbon viscosity, and bound and free fluid volumes, and improved analysis of organic content of a sample or formation, it may also be useful in obtained improved determinations of other sample or formation parameters and content. Further yet, it should be appreciated that the described methods can be adapted and applied to any measurements of longitudinal relaxation time T₁ and diffusion D as well. When the T₁ relaxation is short and is on the order of the polarization time, or when diffusion is large and can significantly reduce the exponent in the diffusion kernel, a correction factor for porosity sensitivity can be computed from the normalized bias obtained from the porosity sensitivity curve. This correction factor can subsequently be used to compute a more accurate value of porosity. Likewise, it will be appreciated that the described methods can be extended to compute correction factors for two and three dimensional measurements such as D-T1, D-T2, T1-T2, D-T1-T2, where D refers to diffusion and T1 and T2 refer to longitudinal and transverse relaxation. It will therefore be appreciated by those skilled in the art that yet other modifications could be made. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses, if any, are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. § 112, paragraph 6 for any limitations of any of the claims herein, except for those in which the claim expressly uses the words ‘means for’ together with an associated function. 

1. A method of investigating a sample, comprising: a) subjecting the sample to a nuclear magnetic resonance (NMR) sequence using NMR equipment; b) using the NMR equipment to detect signals from the sample in response to the NMR sequence; c) analyzing a decay of the signals to extract a distribution of relaxation times or diffusion coefficients; and d) computing a value for a parameter of the sample as a function of at least one of said relaxation times or diffusion coefficients, wherein said computing utilizes a correction factor that modifies the value for the parameter as a function of either the relaxation time for at least short relaxation times or the diffusion coefficient for large diffusion coefficients.
 2. A method according to claim 1, wherein: said parameter is porosity.
 3. A method according to claim 2, wherein: said distribution of relaxation times or diffusions is a distribution of T₂ relaxation times.
 4. A method according to claim 3, wherein: said correction factor is a function of a normalized bias B(T₂) of said NMR equipment.
 5. A method according to claim 4, wherein: said correction factor c_(f)(T₂)=1/(1+B(T₂)).
 6. A method according to claim 5, wherein: ${B\left( T_{2} \right)} \approx \frac{{\hat{\varphi}\left( T_{2} \right)} - \varphi_{T}}{\varphi_{T}}$ where ϕ_(T) and {circumflex over (ϕ)}(T₂) are respectively a true and an estimated porosity for a calibration sample obtained from a porosity sensitivity curve for said NMR tool.
 7. A method according to claim 4, wherein: said correction factor ${c_{f}\left( T_{2} \right)} = {\frac{1}{1 + {{B\left( T_{2} \right)}\frac{R\left( T_{2} \right)}{{\beta {\langle{R\left( T_{2} \right)}\rangle}} + {R\left( T_{2} \right)}}}}\mspace{14mu} {where}}$ ${R\left( T_{2} \right)} = \frac{\hat{\varphi}\left( T_{2} \right)}{\sigma_{\varphi}\left( T_{2} \right)}$ and corresponds to a signal to noise ratio for a given T₂, β is a scalar, < > is an average computed over $T_{2},{{B\left( T_{2} \right)} \approx \frac{{\hat{\varphi}\left( T_{2} \right)} - \varphi_{T}}{\varphi_{T}}}$ is the relative bias obtained from an NMR sensitivity curve for said NMR tool where ϕr is a true porosity of a calibration sample and {circumflex over (ϕ)}(T₂) is an estimated porosity for the calibration sample, and σ_(ϕ) is a standard deviation of the estimated porosity.
 8. A method according to claim 7, wherein: said correction factor tends to a value of 1 for a particular T₂ relaxation time when said signal to noise ratio is small for that particular T₂ relaxation time.
 9. A method according to claim 2, further comprising: using said value for porosity obtained utilizing said correction factor to obtain determinations of at least one additional parameter of said sample.
 10. A method according to claim 9, wherein: said at least one additional parameter of said sample is one of rock permeability, hydrocarbon viscosity, bound fluid volume, and free fluid volumes.
 11. A method according to claim 9, wherein: said at least one additional parameter of said sample is organic content of said sample.
 12. A method according to claim 9, wherein: said at least one additional parameter of said sample is montmorillonite content.
 13. A method according to claim 1, wherein: said sample is a geological formation traversed by a borehole, and said method further comprises repeating a)-d) at a plurality of locations along the borehole. 14-23. (canceled)
 24. A method of investigating a sample, comprising: a) subjecting the sample to a nuclear magnetic resonance (NMR) sequence using NMR equipment; b) using the NMR equipment to detect signals from the sample in response to the NMR sequence; c) analyzing the decay of the signals to extract a distribution of at least one of D-T1, D-T2, T1-T2, and D-T1-T2 multi-dimensional measurements where D indicates a diffusion coefficient, T1 indicates longitudinal relaxation times, and T2 indicates transverse relaxation times; and d) computing a value for a parameter of the sample as a function of at least one multi-dimensional measurement, wherein said computing utilizes a correction factor that modifies the value for the parameter as a multi-dimensional function of diffusion and relaxation times or longitudinal and traverse relaxation times for at least short relaxation times and large diffusion coefficients. 